Tumor Characteristics of Mohs Surgery Patients in Ottawa, Canada Versus Houston, Texas—A Consequence of Access to Care?
Bibliographic record
Abstract
BACKGROUND: Ontario is one of the most underserved provinces in Canada for providing Mohs micrographic surgery (MMS). A new MMS clinic was opened in Ottawa, Ontario, in June 2009 to help combat the increasing incidence of nonmelanoma skin cancer (NMSC) in this region. OBJECTIVE: To prospectively compare MMS cases completed in Ottawa with cases completed in Houston, Texas, and examine the differences in tumor characteristics. MATERIALS AND METHODS: The first 150 cases performed in Ottawa were prospectively compared with 150 consecutive cases performed at a Mohs surgery clinic in Houston, Texas. Patient demographics, tumor diagnosis, primary or recurrent disease, tumor dimension, number of surgical stages, defect size, complexity of the procedure, and closure method were compared. RESULTS: The average preoperative tumor area was three times as great in Ottawa as in Houston. Almost one entire additional stage was required to clear the tumors treated in Ottawa. Postoperative defects were 87% larger in Ottawa. The number of advanced reconstructive repairs was significantly higher in Ottawa (93%) than Houston (14%). CONCLUSIONS: A significantly higher NMSC disease burden and greater surgical complexity was observed in the tumors treated in Ottawa than in Houston.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".